Protein Structure Prediction
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1 Protein Structure Prediction Michael Feig MMTSB/CTBP 2009 Summer Workshop
2 From Sequence to Structure SEALGDTIVKNA
3 Folding with All-Atom Models AAQAAAAQAAAAQAA All-atom MD in general not succesful for real proteins because of insufficient sampling but also force field issues. CHARMM force field Implicit solvent replica exchange simulations 8 replicas, 10 ns/replica Michael Feig
4 Ab initio Structure Prediction Protocol Amino Acid Sequence Sampling to generate native-like structures Scoring & Clustering to select native-like structures 3D Structure
5 Improved Ab initio Prediction Protocol Amino Acid Sequence Secondary Structure Prediction PSIPRED et al. Efficient Sampling e.g. MONSSTER/SICHO to generate native-like structures All-Atom Reconstruction Scoring & Clustering e.g. MMGB/SA, DFIRE to select native-like structures 3D Structure
6 Conformational Sampling vs. Scoring SICHO sampling of protein A All-Atom Energy in kcal/mol RMSD from native in Å
7 Realistic Example from CASP CHARMM22/GBMV C RMSD in Å (62 residues)
8 Sampling, Scoring, Clustering CHARMM22/GBMV C RMSD in Å (62 residues)
9 Ab initio Predictions DNase fragmentation factor NMR structure 1KOY Best-scoring prediction, 7.4 Å Problems with secondary structure prediction and sampling. Scoring luckily worked here.
10 Secondary Structure Prediction GDPIVKNAKLDSRLANKEALRLL?
11 Secondary Structure Propensities -helix -sheet turn Chou & Fasman (1974)
12 Secondary Structure Prediction Methods SABLE 77.6% PSIPRED 76.2% PSSP 75.1% SAM-T99-sec 76.1% PHD 72.3% C+F 50-60% C+F: Chou & Fasman GOR: Garnier, Osguthorpe, Robson Improved accuracy is obtained via known structural information from homologous proteins
13 70 60 Secondary structure prediction Per-Residue Accuracy <Q3>=72.3% ; sigma=10.5% Number of protein chains stu 1psm 3ifm 1spf 1bct Per-residue accuracy (Q 3 )
14 Sampling Could we have reached the native state? CHARMM22/GBMV C RMSD in Å (62 residues)
15 Random Sampling Random sampling of protein-like conformations: probability 2 RMSD ( r RMSD ) Pr R e dr 0 RMSD 3.333N res 1/3 For N=62: <RMSD> = 13.2 Å A B so where are doing better than random sampling! - r -r +r RMSD from native Reva BA, Finkelstein AV, Skolnick J. Folding & Design (1998) 3:
16 Can we Reach the Native State? Iterative refinement with ideal scoring function: RMSD from native? RMSD
17 Ideal Sampling with MD 1 ps MD w/ CHARMM19/EEF samples at each cycles 2.6 Å T0132 initial: 6.7 Å Stumpff-Kane, Maksimiak, Lee, Feig: Proteins (2008) 70,
18 Can we reach the native? Resolution limit of about Å Sampling? Force field? Stumpff-Kane, Maksimiak, Lee, Feig: Proteins (2008) 70,
19 How about coarse-grained sampling? T0132 MD all-atom/impl. solvent 1 ps 300K 1000 samples/cycle SICHO coarse-grained/lattice MC sampling 1000 samples/cycle V F ma Kolinski & Skolnick: Proteins 32, 475 (1998)
20 Can we reach the native? Resolution limit of about >3 Å! SICHO Stumpff-Kane, Maksimiak, Lee, Feig: Proteins (2008) 70,
21 Scoring Can we pick out the native state? CHARMM22/GBMV C RMSD in Å (62 residues)
22 Scoring Functions Knowledge-based/statistical derived from known protein structures limited by training data usually fast e.g. DOPE, DFIRE, OPUS-PSP, RAPDF, prosaii Force field based model physical energy landscape more robust and transferable often expensive (require minimization) e.g. MMPB(GB)/SA, UNRES
23 -3500 Scoring Function Comparison MMGB/SA vs. DFIRE MMGB/SA score DFIRE score C RMSD in Å radius of gyration C RMSD in Å C RMSD in Å
24 Reliable and accurate ab initio structure prediction may eventually succeed
25 but the solution may lie elsewhere.
26 Structure Prediction Approaches Sequence Align to sequences of known structures Build extended chain Build model(s) using aligned template(s) Simulated ab initio Folding Template-Based Modeling Prediction Ab initio prediction
27 Sequence Homology Human thioredoxin (1AUC) SDKIIHLTDDSFDTDVLKADGAILVDFWAEWCGPCKMIAPILDEIADEYQGKLTVA : :....:..::: : :::::::: :.....:.... MVKQIESKTAFQEALDAAGDKLVVVDFSATWCGPCKMIKPFFHSLSEKYSNVIFL- KLNIDQNPGTAPKYGIRGIPTLLLFKNGEVAATKVGALSKGQLKEFLDAN---LA...:..:....::..::.:. :::.: : :: :.:. :. EVDVDDCQDVASECEVKCMPTFQFFKKGQ----KVGEFS-GANKEKLEATINELV E. Coli thioredoxin (1THO)
28 Comparative Modeling Assumption: Proteins with similar sequence have similar structure Human thioredoxin (1AUC) E. Coli thioredoxin (1THO)
29 Structural Templates from Homology Challenges: Template identification Correct alignment Loop modeling All-atom reconstruction (side chain rebuilding) PGTAPKYGIRGIPTLLLFKNGEVAATKVGALSKGQLKEFLDAN---LA.:....::..::.:. :::.: : :: :.:. :. QDVASECEVKCMPTFQFFKKGQ----KVGEFS-GANKEKLEATINELV
30 Template-Based Model Building Direct template-based modeling: -Use C -trace from template to build backbone - Add sidechains to backbone (using rotamer libraries) Restraint-based modeling: - Use one or more templates to setup list of restraints -Sample C -trace, CG model (SICHO), or all-atom model (Modeller) from extended chain with force field under template-derived restraints - Reconstruct all-atom model Restraint-based modeling allows use of multiple templates and can reflect structural variations for different sequences.
31 Accuracy of Predictions by Homology 100 % predictions % sequence identity <2Å RMSD <5Å RMSD
32 Prediction through Fold Recognition Assumption: Proteins with similar secondary structure share fold 1N91 1JRM
33 Templates through Fold Recognition Challenges: Wrong templates Alignment uncertain Fragment modeling Refinement needed
34 Extreme Homology Modeling: TASSER Find fragments of varying sizes (typically residues) based on sequence alignment or fold recognition Jam fragments together in coarse-grained sampling procedure (Monte Carlo enhanced with replica exchange) Score resulting structures based on clustering Method very successful, especially for hard cases when no overall template is available. J. Skolnick, Y. Zhang
35 Extreme Homology Modeling: Rosetta Find short fragments (9 and 3 residues) based on sequence alignment or fold recognition Sample fragment assembly at C level with Monte Carlo procedure Generate very large number of structures (10,000-1,000,000) and score with series of filtering functions Method also very successful for hard cases. D. Baker
36 State of the Art in Structure Prediction CASP8 Results
37 CASP8: Human/Server TBM Targets 1 DBAKER Rosetta+expert/Baker 2 Zhang TASSER+servers/Zhang 3 SAM-T08-human SAM+expert/Karplus 4 fams-ace Meta/Umeyama 5 TASSER TASSER+expert?/Skolnick 6 MULTICOM HHSearch+MultiMod+Meta/Cheng 7 McGuffin Meta/McGuffin 8 ZicoFullSTPFullData Meta/Girgis 9 ZicoFullSTP Meta/Girgis 10 Zhang-Server 426s TASSER/Zhang 11 Zico Meta/Girgis 12 IBT_LT COMA+expert/Venclovas 13 Chicken_George SimFold+use servers/chikenji 14 mufold Rosetta/Xu 15 pro-sp3-tasser 409s TASSER/Skolnick 16 GeneSilico Meta/Bujnicki 17 A-TASSER TASSER/Skolnick 18 Sternberg HHSearch+MultiMod+expert?/Sternberg 19 Jones-UCL Jones 20 BAKER-ROBETTA 425s Rosetta/Baker 21 fais@hgc Shirota 22 RAPTOR 438s Xu 23 LevittGroup Levitt 24 LEE MultiMod+CSA?/Lee 25 keasar ?Keasar 26 Bates_BMM Bates 27 Phyre_de_novo 322s HHSearch+MultiMod+Poing/Sternberg 28 3DShot Seth 29 POEMQA Wenzel 30 MidwayFolding Sosnick 31 METATASSER 182s TASSER/Skolnick 32 Elofsson Elofsson 33 MULTICOM-CLUSTER 020s HHSearch+MultiMod/Cheng 34 MUSTER 408s /Zhang 35 3DShotMQ Seth 36 Phyre2 235s HHSearch+MultiMod/Sternberg 37 MULTICOM-REFINE 013s HHSearch+COMPASS+MultiModCheng 38 SAMUDRALA Samudrala 39 FEIG 166s TASSER+Rosetta+MultiMod/Feig 40 PS2-manual Chen Rosetta TASSER Meta-Method Human expert Targets where template(s) could be (easily) found in PDB
38 CASP8: Human/Server FM Results 1 DBAKER keasar MULTICOM MUFOLD-MD 404s BAKER-ROBETTA 425s Zico ZicoFullSTPFullData ZicoFullSTP LevittGroup mufold Zhang POEMQA fams-ace SAM-T08-human Zhang-Server 426s Jones-UCL Bates_BMM McGuffin PSI 385s POEM ABIpro FALCON 351s A-TASSER RBO-Proteus 479s TASSER Sternberg pro-sp3-tasser 409s Chicken_George FALCON_CONSENSUS 220s Bilab-UT Sasaki-Cetin-Sasai TJ_Jiang METATASSER 182s fais-server 116s DShotMQ MULTICOM-CLUSTER 020s DShot MUProt 443s LEE MULTICOM-REFINE 013s FEIG 166s Rosetta TASSER Meta-Method Human expert Targets where template could not (easily) be found in PDB
39 Questions?
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